With the rapid development of high-speed communication and artificial intelligence
technologies, human perception of real-world scenes is no longer limited to the use of small …

In the near future, autonomous vehicles with full self-driving features will populate our public
roads. However, fully autonomous cars will require robust perception systems to safely …

Densely annotating LiDAR point clouds is costly, which often restrains the scalability of fully-
supervised learning methods. In this work, we study the underexplored semi-supervised …

We present recent advancements of optical phased array LiDAR with record-performance
system demonstrations. First, we give an overview of the technology and how it combines …

Efficient data utilization is crucial for advancing 3D scene understanding in autonomous
driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully …

Accurate depth-sensing is crucial in securing a high success rate of robotic harvesting in
natural orchard environments. The solid-state LiDAR technique, a recently introduced …

Integrating sensing functions into future mobile equipment has become an important trend.
Realizing different types of sensing and achieving mutual enhancement under the existing …

LiDAR is an indispensable sensor for autonomous driving that can provide precise 3D
information about the environment. Among various types of LiDARs, mechanical LiDARs are …

Active learning, a label-efficient paradigm, empowers models to interactively query an oracle
for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem …

This article provides a tutorial introduction to the direct Time-of-Flight (dToF) signal chain
and typical artifacts introduced due to detector and processing electronic limitations. We …